Vote/Veto Classification, Ensemble Clustering and Sequence Classification for Author Identification

The Author Identification task for PAN 2012 consisted of three different sub-tasks: traditional authorship attribution, authorship clustering and sexual predator identification. We developed three machine learning approaches for these tasks. For the two authorship related tasks we created various sets of feature spaces, where individual differences in writing styles are assumed to surface in just a subset of these spaces. The challenge there was to combine these feature spaces to enable the machine learning algorithms to detect these differences across multiple feature spaces. In the case of authorship attribution we combined the results of multiple base classifiers by following a supervised vote/veto meta classifier approach. For the intrinsic plagiarism/authorship clustering subtask we used an unsupervised ensemble clustering approach in order to combine information from several feature spaces. In the sexual predator identification task we applied a supervised sequence classification approach to uncover temporal patterns within chat conversations by categorizing not only the offending messages, but also the reactions to these offending messages.